Data in some systems such as enterprise systems can grow at a rapid pace. The underlying database tables that store the data can become very large. Performance of a system is typically dependent on the size of database tables. Large database tables make operations such as searching and reading slower, resulting in decreased productivity. Therefore, some data records from database tables are moved to a storage system to increase productivity. This process of moving data records from a database table to a storage system is called archiving. Data records for archiving are identified based on various parameters. For example, data records that do not need to be accessed frequently can be selected for archiving. However, different systems can have different volumes of data. Therefore, manual analysis of data is typically required to determine an optimal archiving schedule. Such manual analysis can be time consuming and inefficient. For example, if data usage pattern changes, then a new analysis needs to be performed.